Oral candidiasis and potential risk factors among disabled and nondisabled in Al-Baha region, Saudi Arabia

BACKGROUND Oral candidiasis(OC)is an oral health disease that could influence patients’oral health quality of life.AIM To estimate prevalence of OC among disabled and non-disabled individuals and its potential risk factors in the Al-Baha region,Saudi Arabia.METHODS An observational cross-sectional study was carried out among 148 disabled and non-disabled participants.The technique of concentrated oral rinse employing the Sabouraud Dextrose Agar medium accompanied with 0.05%chloramphenicol was conducted to assess and isolate candida.Oral examination using the World Health Organization guidelines was conducted to examine participants’oral hea-lth status.A pre-designed questionnaire was also used to evaluate sociodemo-graphic,medical history,and oral hygiene habits of the studied population.RESULTS Out of 148 participants(n=57,38%)had colonized candida.None of the studied population had visible Candida lesions.However,Candida was found in the oral rinses without the subject presenting any lesions or issues caused by Candida(asymptomatic colonization).The most common prevalent OC among participants were Candida albicans,Candida glabrata,Candida dubliniensis,Candida krusei,Candida tropicalis,and Candida parapsilosis(n=35,61%;n=8,14%;n=6,10%;n=5,9%;n=2,4%;and n=1,2%)respectively.Diabetes,smoking,poor plaque,and gingival status were key potential risk factors that significantly associated with candida’s density and presence(P=0.001,P=0.001,P=0.01,and P=0.01)respectively.Disability status had no statistically significant effect on presence and density of Candida.CONCLUSION The prevalence of OC is almost third of the studied population;thus,may provoke a need to develop preventive strategies to reduce the OC rate and establish solid treatment plans.

Study of the Magnetocaloric Effect in La0.5Sm0.2Sr0.3Mn1-xFexO3 (x = 0 and 0.05) Manganites with the Mean-Field Theory

In this paper, the magnetocaloric in La0.5Sm0.2Sr0.3Mn1-xFexO3 compounds with x = 0 (LSSMO) and x = 0.05 (LSSMFO) were simulated using mean field model theory. A strong consistency was observed between the theoretical and experimental curves of magnetizations and magnetic entropy changes, −ΔSM(T). Based on the mean-field generated −ΔSM(T), the substantial Temperature-averaged Entropy Change (TEC) values reinforce the appropriateness of these materials for use in magnetic refrigeration technology within TEC (10) values of 1 and 0.57 J∙kg−1∙K−1under 1 T applied magnetic field.

Improving Multiple Sclerosis Disease Prediction Using Hybrid Deep Learning Model

Myelin damage and a wide range of symptoms are caused by the immune system targeting the central nervous system in Multiple Sclerosis(MS),a chronic autoimmune neurological condition.It disrupts signals between the brain and body,causing symptoms including tiredness,muscle weakness,and difficulty with memory and balance.Traditional methods for detecting MS are less precise and time-consuming,which is a major gap in addressing this problem.This gap has motivated the investigation of new methods to improve MS detection consistency and accuracy.This paper proposed a novel approach named FAD consisting of Deep Neural Network(DNN)fused with an Artificial Neural Network(ANN)to detect MS with more efficiency and accuracy,utilizing regularization and combat over-fitting.We use gene expression data for MS research in the GEO GSE17048 dataset.The dataset is preprocessed by performing encoding,standardization using min-max-scaler,and feature selection using Recursive Feature Elimination with Cross-Validation(RFECV)to optimize and refine the dataset.Meanwhile,for experimenting with the dataset,another deep-learning hybrid model is integrated with different ML models,including Random Forest(RF),Gradient Boosting(GB),XGBoost(XGB),K-Nearest Neighbors(KNN)and Decision Tree(DT).Results reveal that FAD performed exceptionally well on the dataset,which was evident with an accuracy of 96.55%and an F1-score of 96.71%.The use of the proposed FAD approach helps in achieving remarkable results with better accuracy than previous studies.

ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)scans.These factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma progression.This study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation accuracy.ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms.This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range dependencies.By doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor boundaries.We rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 datasets.Notably,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse datasets.Furthermore,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset size.Radiomic features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival prediction.This model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing methods.This ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient survival.Importantly,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

Identification of Sudanese Sesame Oil Quality Using a 680 nm Semiconductor Laser Spectrometer and Other Techniques

This study investigates the optical properties of sesame oil from traditional and industrial sources using a custom-designed semiconductor laser spectrometer, UV-Vis spectroscopy, and FTIR spectroscopy. Six samples were collected from traditional presses and factories in Khartoum State and White Nile State. The spectrometer, constructed with a 680 nm semiconductor laser and various resistor values, measured the absorbance of sesame oil samples. UV-Vis spectroscopy identified absorbance peaks at 670 nm and 417 nm, corresponding to chlorophyll a and b. FTIR analysis showed nearly identical spectra among the samples, indicating similar chemical compositions. Laser spectrometer analysis revealed specific absorbance values for each sample. The results highlight the feasibility of using a 680 nm semiconductor laser for analyzing sesame oil, providing a cost-effective alternative to other wavelengths. This study demonstrates the potential of integrating traditional methods with modern spectroscopic techniques for the quality assessment of sesame oil.

Forecasting Diabetes Patients Attendance at Al-Baha Hospitals Using Autoregressive Fractional Integrated Moving Average (ARFIMA) Models

Diabetes has become a concern in the developed and developing countries with its growing number of patients reported to the ministry of health records. This paper discusses the use of the Autoregressive Fractional Moving Average (ARFIMA) technique to modeling the diabetes patient’s attendance at Al-Baha hospitals using monthly time series data. The data used in the analysis of this paper are monthly readings of diabetes patients data covered the period January 2006-December 2016. The data were collected from the General Directorate of Health Affairs, Al-Baha region. The autoregressive fractional moving average approach was applied to the data through the model identification, estimation, diagnostic checking and forecasting. Hurst test results and ACF confirmed that there is a long memory behavior in diabetic patient’s data. Also, the fractional difference to diabetes series data revealed that (d = 0.44). Moreover, unit root tests indicated that the fractional difference of diabetes series level is stationary. Furthermore, according to AIC and BIC of model selection criteria ARFIMA (1, 0.44, 0) model shown the smallest values, hence this model was chosen as an adequate represents the data. Also, a diagnostic check confirmed that ARFIMA was appropriate and highly recommended in modeling and forecasting this type of data.

Three-dimensional bioprinting in ophthalmic care

Three-dimensional(3D)bioprinting is widely used in ophthalmic clinic,including in diagnosis,surgery,prosthetics,medications,drug development and delivery,and medical education.Articles published in 2011–2022 into bioinks,printing technologies,and bioprinting applications in ophthalmology were reviewed and the strengths and limitations of bioprinting in ophthalmology highlighted.The review highlighted the trade-offs of printing technologies and bioinks in respect to,among others,material type cost,throughput,gelation technique,cell density,cell viability,resolution,and printing speed.There is already widespread ophthalmological application of bioprinting outside clinical settings,including in educational modelling,retinal imaging/visualization techniques and drug design/testing.In clinical settings,bioprinting has already found application in pre-operatory planning.Even so,the findings showed that even with its immense promise,actual translation to clinical applications remains distant,but relatively closer for the corneal(except stromal)tissues,epithelium,endothelium,and conjunctiva,than it was for the retina.This review similarly reflected on the critical on the technical,practical,ethical,and cost barrier to rapid progress of bioprinting in ophthalmology,including accessibility to the most sophisticated bioprinting technologies,choice,and suitability of bioinks,tissue viability and storage conditions.The extant research is encouraging,but more work is clearly required for the push towards clinical translation of research.

Altered Levels of Blood Glucose and Serum Lipids in Sudanese Patients with Ovarian Cancer

Background: The etiology of ovarian cancer is not well-understood;numerous metabolomics profiling, epidemiological, and hospital-based case control studies have associated abnormal levels of blood glucose and serum lipids with the risk and the prognosis of various types of cancers including ovarian cancer. The association between the risk of the incidence of ovarian cancer and the alterations in the levels of blood glucose and serum lipids is not well defined. Objective: In this study we aimed to compare the levels of blood glucose, triacylglycerols, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol in patients with different stages of ovarian cancer and healthy controls to determine how they relate to the risk and prognosis of ovarian cancer. Methodology: In a case-control cross sectional study, we enrolled ninety-nine Sudanese women, diagnosed with ovarian cancer but had not received any kind of treatment as the study group, and a control group of forty-one age-matched, apparently healthy women. The patients were classified according to the International Federation of Obstetricians and Gynecologists staging system into two groups: early stages (stage I & II) and late stages (stages III & IV). Blood glucose and serum lipids;triacylglycerols, total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol were determined by enzymatic colorimetric methods using commercially available analytical kits. The IBM SPSS version 20 software was used for statistical analysis. A Mann-Whitney U test was used for comparison of the median concentrations of blood glucose, triacylglycerols, total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol in the study groups. Logistic regression model was used to estimate the relative risk of ovarian cancer in relation to levels of blood glucose and serum lipids. P value of 0.05 was considered significant. Results: Our data indicated significantly higher levels of blood glucose (p < 0.001), triacylglycerols (p = 0.002), and low-density lipoprotein cholesterol (p < 0.001), and lower levels of high-density lipoprotein cholesterol (p = 0.023), in ovarian cancer patients compared to the control subjects. No significant difference was found in the levels of blood glucose or any of the serum lipids between patients in the early stages (stage I & II) and those in late stages (stage III & IV) of ovarian cancer. The logistic regression analysis indicated significant association between the elevated levels of the blood glucose, triacylglycerols and low-density lipoprotein cholesterol and the risk of the ovarian cancer. Conclusion: We conclude that the levels of blood glucose, triacylglycerols, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol differ significantly between ovarian cancer patients and the healthy control subjects. The risk of ovarian cancer was positively associated with the levels of blood glucose, triacylglycerols and low-density lipoprotein cholesterol, and negatively associated with levels of high-density lipoprotein cholesterol. Therefore, determination of blood glucose and serum lipids, particularly, triacylglycerols, low-density lipoprotein cholesterol may be helpful as diagnostic indicators of ovarian cancer (OC).

Mathematical Morphology View of Topological Rough Sets and Its Applications

This article focuses on the relationship between mathematical morphology operations and rough sets,mainly based on the context of image retrieval and the basic image correspondence problem.Mathematical morphological procedures and set approximations in rough set theory have some clear parallels.Numerous initiatives have been made to connect rough sets with mathematical morphology.Numerous significant publications have been written in this field.Others attempt to show a direct connection between mathematical morphology and rough sets through relations,a pair of dual operations,and neighborhood systems.Rough sets are used to suggest a strategy to approximatemathematicalmorphology within the general paradigm of soft computing.A single framework is defined using a different technique that incorporates the key ideas of both rough sets and mathematical morphology.This paper examines rough set theory from the viewpoint of mathematical morphology to derive rough forms of themorphological structures of dilation,erosion,opening,and closing.These newly defined structures are applied to develop algorithm for the differential analysis of chest X-ray images from a COVID-19 patient with acute pneumonia and a health subject.The algorithm and rough morphological operations show promise for the delineation of lung occlusion in COVID-19 patients from chest X-rays.The foundations of mathematical morphology are covered in this article.After that,rough set theory ideas are taken into account,and their connections are examined.Finally,a suggested image retrieval application of the concepts from these two fields is provided.

A Comparison of Four Methods of Estimating the Scale Parameter for the Exponential Distribution

In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.

Ranking of Web Pages in a Personalized Search

The basic idea behind a personalized web search is to deliver search results that are tailored to meet user needs, which is one of the growing concepts in web technologies. The personalized web search presented in this paper is based on exploiting the implicit feedbacks of user satisfaction during her web browsing history to construct a user profile storing the web pages the user is highly interested in. A weight is assigned to each page stored in the user’s profile;this weight reflects the user’s interest in this page. We name this weight the relative rank of the page, since it depends on the user issuing the query. Therefore, the ranking algorithm provided in this paper is based on the principle that;the rank assigned to a page is the addition of two rank values R_rank and A_rank. A_rank is an absolute rank, since it is fixed for all users issuing the same query, it only depends on the link structures of the web and on the keywords of the query. Thus, it could be calculated by the PageRank algorithm suggested by Brin and Page in 1998 and used by the google search engine. While, R_rank is the relative rank, it is calculated by the methods given in this paper which depends mainly on recording implicit measures of user satisfaction during her previous browsing history.

The Influence of Fabric Structural Parameters on Dust Retention Using a Simple, Newly Constructed Device

This article has been directed to Environment Protection Technology with the purpose of providing a new instrument designed and developed to measure filtration efficiency through the relationship between clean cloth fabric structural parameters, dust parameters, and test measuring variables. Fabric samples used throughout the present study were woven cotton 100%, polyester 100% and cotton/polyester 50/50%. The warp count: 30/2 for all fabric samples, the weft count is 9/1;12/1;and 20/1. The weave of satin, basket, and twill is 3/1 with four different picks/cm to produce the fabrics with the same cover factor. For dust separation fabrics range in weight from “300 - 450 g/m2” with an air permeability of “100 - 300 l/d m2·min” at “196.2 Pa” (20 mm WG) as specified in DIN 53887. Air permeability through fabrics depends entirely on the sieving percent of the surface of the fabric, which is partly the pores and partly the permeability through the yarns, which are the basic elements of a fabric. The results showed that dust capturing depends entirely on air permeability, which is related to fabric weave structure and fabric material at specified testing and measuring variables.

Evaluation of Cobalt Application Combined with Gypsum and Compost as a Regulator of Cabbage Plant Tolerance to Soil Salinity

In response to the global food crisis and the imperative to address soil degradation, the international agricultural policy is actively working to alleviate the adverse impacts of soil salinity. As part of this initiative, a field trial spanning two consecutive seasons (2019/20-2020/21) was conducted under saline conditions. The primary objective was to evaluate the influence of various compost sources, including vermicompost at a rate of 0.5 ton·fed-1 and plant residues compost at a rate of 5.0 ton·fed-1, as main plots. Subplots were established by applying agricultural gypsum, both in the presence and absence of gypsum requirements. Additionally, sub-subplots were created by externally applying cobalt at a rate of 10.0 mg·L-1, with one sub-subplot receiving foliar cobalt application and the other not. The trial sought to assess the growth performance, chemical composition, enzymatic antioxidants, yield, and quality of cabbage plants (Brassica oleracea var. capitata L.) cultivated in saline soil. According to the findings, cabbage plants exhibited the most favorable response in terms of plant height, chlorophyll content, carotene levels, leaf area, nitrogen (N), phosphorus (P), potassium (K), head yield, vitamin C, and total dissolved solids (TDS) when treated with vermicompost, followed by plant compost. Conversely, plants grown without compost exhibited the least improvement in performance. Cabbage treated with agricultural gypsum requirements showed better performance than those without gypsum amendment. Moreover, plants subjected to cobalt spray demonstrated the highest growth, yield, and quality parameters compared to those without cobalt foliar application. In contrast, the control group (plants without the studied treatments) displayed the highest levels of enzymatic antioxidants, specifically catalase and peroxidase. This indicates that soil salinity stress led to an increase in catalase and peroxidase production in cabbage plants as a defense against the harmful impact of reactive oxygen species (ROS) resulting from soil salinity stress. The applied treatments (compost, gypsum, and cobalt) led to a reduction in the cabbage plant’s inherent production of catalase and peroxidase. Generally, the combined treatment of vermicompost × gypsum requirements × cobalt proved effective in mitigating the detrimental effects of soil salinity on cabbage plants. These findings hold significance for farmers and policymakers aiming to enhance agricultural productivity in regions affected by soil salinity. Additionally, further research can explore the long-term effects of these treatments on soil health and crop sustainability.

The past, current and future trends in DNA vaccine immunisations

This review focuses on DNA vaccines, denoting the last two decades since the early substantiation of preclinical protection was published in Science in 1993 by Ulmer et al. In spite of being safely administered and easily engineered and manufactured DNA vaccine, it holds the future prospects of immunization by inducing potent cellular immune responses against infectious and non-infectious diseases. It is well documented that injection of DNA plasmid encoding a desired gene of interest can result in the subsequent expression of its products and lead to the induction of an immune response within a host. This is pertinent to prophylactic and therapeutic vaccination approach when the peculiar gene produces a protective epitope from a pathogen. The recent studies demonstrated by a number of research centers showed that these immune responses evoke protective immunity against several infectious diseases and cancers, which provides adequate support for the use of this approach. We attempt in this review to provide an informative and unbiased overview of the general principles and concept of DNA vaccines technology with a summary of a novel approach to the DNA vaccine, present investigations that describe the mechanism(s) of protective immunity provoked by DNA immunization and to highlight the advantages and disadvantages of DNA immunisation.

Evaluation of anti-resistant activity of Auklandia(Saussurea lappa) root against some human pathogens

Objective:The antimicrobial activity of the ethanol extract of the Auklandia(Saussurea lappa)root plant was investigated to verify its medicinal use in the treatment of microbial infections.Methods:The antimicrobial activity of the ethanol extract was tested against clinical isolates ofsome multidrug-resistant bacteria using the agar well diffusion method.Commercial antibioticswere used as positive reference standards to determine the sensitivity of the clinical isolates.Results:The extracts showed significant inhibitory activity against clinical isolates of methicillinresistantStaphylococcus aureus,Pseudomonas aeruginosa,Escherichia coli,Klebsiella pneumonia,Extended Spectrum Beta-Lactemase,Acinetobacter baumannii.The minimum inhibitory concentration values obtained using the agar dilution test ranged from 2.0μg/μL-12.0μg/μL.In the contrary the water extract showed no activity at all against the tested isolates.Furthermore,theresults obtained by examining anti-resistant activity of the plant ethanolic extract showed thatat higher concentration of the plant extract(12μg)all tested bacteria isolates were inhibited with variable inhibition zones similar to those obtained when we applied lower extract concentrationusing the well diffusion assay.Conclusion:The results demonstrated that the crude ethanolicextract of the Auklandia(Saussurea lappa)root plant has a wide spectrum of activity suggestingthat it may be useful in the treatment of infections caused by the above clinical isolates(humanpathogens).

Antimicrobial and Catalytic Activities of Green Synthesized Silver Nanoparticles Using Bay Laurel (<i>Laurus nobilis</i>) Leaves Extract

In this study, bay laurel extract (BLE) used as a reducing and capping agent for the synthesis of silver nanoparticles (AgNPs). The green-prepared AgNPs investigated using UV-visible spectroscopy, Fourier-transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), Scanning Electron Microscopy with Energy Dispersive X-ray (SEM-EDX) and Transmission electron microscopy (TEM). Formation of AgNPs monitored at ambient temperature by a change in color from the starting solution to dark brown. Green synthesis AgNps were investigated for antimicrobial activity. The microorganisms employed were E. coli, K. pneumoniae, B. cereus, S. aureus, C. lbicans and Aspergillus. The susceptibility of microorganisms against the six AgNPs solutions was determined using the disk diffusion method. The catalytic activity of the prepared AgNPs (sample, d) for basic brown 1 dye was investigated. The results showed the characteristic surface plasmon resonance peak of the AgNPs appeared at approximately 415 - 440 nm. XRD revealed peaks at 38.2, 44.16, 64.24 and 77.22 &#415;, and the intensity of these peaks enhanced when using microwave curing compared to ambient temperature. SEM and TEM results showed that the silver nano particles have a spherical shape and the particle size for samples is less than 34 nm. FTIR spectroscopy measurements showed the binding of organic compounds on the surface of the silver nanoparticles. Highest antibacterial activity was enhanced with increasing of AgNPs dose and with increasing of extract ration against most of microorganisms except. Removal of basic brown 1 dye by the prepared AgNPs indicated complete dye removal after 8 h.

Optimal Seat and Suspension Design for a Half-Car with Driver Model Using Genetic Algorithm

This paper presents an optimal vehicle and seat suspension design for a half-car vehicle model to reduce human-body vibration (whole-body vibration). A genetic algorithm is applied to search for the optimal parameters of the seat and vehicle suspension. The desired objective is proposed as the minimization of a multi-objective function formed by the combination of seat suspension working space (seat suspension deflection), head acceleration, and seat mass acceleration to achieve the best comfort of the driver. With the aid of Matlab/Simulink software, a simulation model is achieved. In solving this problem, the genetic algorithms have consistently found near-optimal solutions within specified parameters ranges for several independent runs. For validation, the solution obtained by GA was compared to the ones of the passive suspensions through sinusoidal excitation of the seat suspension system for the currently used suspension systems.

Mathematical Model Validation of Search Protocols in MP2P Networks

Broadcasting is a basic technique in Mobile ad-hoc network(MANET),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive flooding technique,floods the network with query messages,while the random walk technique operates by contacting the subsets of every node’s neighbors at each step,thereby restricting the search space.One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource.Many earlier works have mainly focused on the simulation-based analysis of flooding,and its variants under a wired network.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of P2P systems running over MANET.In this paper,we describe how P2P resource discovery protocols perform badly over MANETs.To address the limitations,we propose a new protocol named ABRW(Address Broadcast Random Walk),which is a lightweight search approach,designed considering the underlay topology aimed to better suit the unstructured architecture.We provide the mathematical model,measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques.Further,we also derive three relevant search performance metrics,i.e.,mean no.of steps needed to find a resource,the probability of finding a resource,and the mean no.of message overhead.We validated the analytical expressions through simulations.The simulation results closely matched with our analyticalmodel,justifying our findings.Our proposed search algorithm under such highly dynamic self-evolving networks performed better,as it reduced the search latency,decreased the overall message overhead,and still equally had a good success rate.

Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

One of the key challenges in ad-hoc networks is the resource discovery problem.How efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search space.Many earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network scenario.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P networks.In this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT architecture.We provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed technique.We analytically derive 3 relevant key performance measures,i.e.,the avg.number of steps needed to nd a resource,the probability of locating a resource,and the avg.number of messages generated during the entire search process.